Data Analyst: Professional Certificate in Data Analysis
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Welcome to Program: Data Analyst: Professional Certificate in Data Analysis by MTF Institute
Course provided by MTF Institute of Management, Technology and Finance
MTF is the global educational and research institute with HQ at Lisbon, Portugal, focused on business & professional hybrid (on-campus and online) education at areas: Business & Administration, Science & Technology, Banking & Finance.
MTF R&D center focused on research activities at areas: Artificial Intelligence, Machine Learning, Data Science, Big Data, WEB3, Blockchain, Cryptocurrency & Digital Assets, Metaverses, Digital Transformation, Fintech, Electronic Commerce, Internet of Things.
MTF is the official partner of: IBM, Intel, Microsoft, member of the Portuguese Chamber of Commerce and Industry.
MTF is present in 215 countries and has been chosen by more than 620 000 students.
Course Author:
Dr. Alex Amoroso is a seasoned professional with a rich background in academia and industry, specializing in research methodologies, strategy formulation, and product development. With a Doctorate Degree from the School of Social Sciences and Politics in Lisbon, Portugal, where she was awarded distinction and honour for her exemplary research, Alex Amoroso brings a wealth of knowledge and expertise to the table.
In addition to her doctoral studies, Ms. Amoroso has served as an invited teacher, delivering courses on to wide range of students from undergraduate level to business students of professional and executives courses. Currently, at EIMT in Zurich, Switzerland, she lectures for doctoral students, offering advanced instruction in research design and methodologies, and in MTF Institute Ms. Amoroso is leading Product Development academical domain.
In synergy between academical and business experience, Ms. Amoroso achieved high results in business career, leading R&D activities, product development, strategic development, market analysis activities in wide range of companies. She implemented the best market practices in industries from Banking and Finance, to PropTech, Consulting and Research, and Innovative Startups.
Alex Amoroso’s extensive scientific production includes numerous published articles in reputable journals, as well as oral presentations and posters at international conferences. Her research findings have been presented at esteemed institutions such as the School of Political and Social Sciences and the Stressed Out Conference at UCL, among others.
With a passion for interdisciplinary collaboration and a commitment to driving positive change, Alex Amoroso is dedicated to empowering learners and professionals for usage of cutting edge methodologies for achieving of excellence in global business world.
Data analysis also is the process of collecting, cleaning, and organizing data to uncover patterns, insights, and trends that can help individuals and organizations make informed decisions. It involves examining raw data to find answers to specific questions, identify potential problems, or discover opportunities for improvement.
Data analysts transform raw data into actionable insights to help organisations improve operations, strategies, and customer experiences. Core skills include statistical analysis, critical thinking, data visualisation, and proficiency in tools like Excel, SQL, Python, and Tableau.
Learning data analysis skills is crucial for career building in today’s data-driven world, both for professional positions and managers of all levels. Here’s why:
For Professionals:
Increased Employability: Data analysis skills are in high demand across various industries. Professionals with these skills are more likely to secure well-paying jobs and advance in their careers.
Improved Decision-Making: Data analysis enables professionals to make informed decisions based on evidence and insights rather than relying on intuition or guesswork.
Enhanced Problem-Solving: Data analysis helps professionals identify the root causes of problems, develop effective solutions, and track the effectiveness of interventions.
Increased Efficiency and Productivity: By automating tasks and identifying areas for improvement, data analysis can help professionals work more efficiently and increase their productivity.
For Managers:
Strategic Planning: Data analysis provides managers with the insights needed to develop effective strategies, set realistic goals, and track progress towards objectives.
Performance Management: Managers can use data to monitor team performance, identify areas for improvement, and provide targeted feedback to employees.
Risk Management: Data analysis can help managers identify potential risks, assess their impact, and develop mitigation strategies.
Innovation and Growth: By analyzing data on customer behavior, market trends, and competitor activities, managers can identify opportunities for innovation and growth.
Learning data analysis skills is essential for professionals and managers of all levels who want to succeed in today’s data-driven world. These skills can help individuals make better decisions, solve problems more effectively, and contribute to the success of their organizations.
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3PresentationVideo lesson
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4IntroductionVideo lesson
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5Course Slide-deckText lesson
Please, look to attached files
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6Data Collection and AcquisitionVideo lesson
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7Data Cleaning and PreparationVideo lesson
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8Exploratory Data Analysis (EDA)Video lesson
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9Statistical AnalysisVideo lesson
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10Data VisualisationVideo lesson
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11Predictive AnalyticsVideo lesson
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12Data Interpretation and ReportingVideo lesson
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13Data Privacy and EthicsVideo lesson
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14Tools and Software for Data AnalysisVideo lesson
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15Building a Data Analyst PortfolioVideo lesson
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16Career Development and Job Market TrendsVideo lesson
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17Practical exercisesVideo lesson
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18Next StepsVideo lesson
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19Module presentationVideo lesson
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20Module overviewVideo lesson
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21ExcelVideo lesson
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22Excel Practical TaskText lesson
please, download attachments for practing
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23SQLVideo lesson
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24Exercise: Retrieve and Analyze Customer and Order Data with SQLiteText lesson
please, download attachments for practing
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25PythonVideo lesson
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26Handling Missing Data and Analysing the Data with PythonText lesson
please, download attachments for practing
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27R practiceVideo lesson
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28Exercise: Conduct Statistical Analysis Using RText lesson
please, download attachments for practing
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29TableauVideo lesson
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30Exercise: Visualizing Global Earthquake Data with Geographic RepresentationText lesson
please, download attachments for practing
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31Next stepsVideo lesson
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32Introduction to Data-Based Decision Making (DBDM)Text lesson
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33The Data Landscape: Types, Sources, and Quality.Text lesson
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34Data Collection and PreparationText lesson
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35Descriptive Analytics: Understanding the "What"Text lesson
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36Diagnostic Analytics: Exploring the "Why"Text lesson
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37Predictive Analytics: Forecasting the "Future"Text lesson
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38Prescriptive Analytics: Recommending the "How"Text lesson
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39Data-Driven Culture and Organizational ChangeText lesson
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40Tools and Technologies for Data-Based Decision MakingText lesson
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41Case Studies in Data-Based Decision MakingText lesson
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